Artificial intelligence technology is developing at an unprecedented speed around the world. A large number of AI applications have been built in a short period of time. When developers are catching up with the progress, testers also need to keep up with the pace of this era.
Thankfully, with rapid advances in artificial intelligence, new approaches to testing, automation, and quality assurance (QA) are emerging, opening new doors for AI application testing. How should testers test AI and ML applications now and in the future? Here are some of the main methods you should know about.
1. Data quality testing
The data testing process needs to use benchmarks to evaluate the status of the data. Although the goals of each company may be different, high-quality data is usually the core requirement of AI applications:
- Error-free: The data used is free from any problems with structure and format.
- Consolidation: Data is secured in one centralized system instead of scattered across multiple systems.
- Uniqueness: Data is not repeated.
- Timeliness: Information is timely and relevant.
- Accuracy: Provide accurate information to help apps make informed decisions.
Testing data quality means identifying mislabeled, outdated or irrelevant data by comparing enterprise information with established known facts. At this level of testing, it can be as simple as creating a data profile for the dataset, a process known as synthetic data generation. Using the definition validation of this data set, companies can classify whether their data is valid and thus measure its quality.
2. Bias test
Another important test that is gaining in popularity is the bias test. The bias of an AI system is largely determined by the data it collects.
For example, a 2016 report found that Amazon favors male IT applicants. When the e-commerce giant trains its artificial intelligence bots to find the best candidates for the job, it uses the resumes of existing employees, which are mostly men, as a database. Based on this information, their AI speculated that only male candidates would make the best IT employees, which was not the case.
To avoid making the same mistake, you should test for bias when pushing your algorithm online.
Back in 2016, deviation testing was simply a matter of analyzing requirements to establish an appropriate response to a set of inputs. Now, it's not as clear cut. You need more variety and more options. You want to create multiple test cases to consider all possible variables, rather than using one data set to generate only one scenario. While the results may not always be perfect, they still offer a better, fairer, and more comprehensive approach to combating bias and developing more inclusive AI applications.
3. AI model evaluation and testing
AI model evaluation and testing helps you predict the results of analysis and evaluation, and it involves three steps: In the first phase of AI testing, the collected data is divided into training set, validation set, and test set. The training set contains up to 75% of the dataset and assigns model weights and biases. The validation set consists of 15% to 20% of the data during training to assess initial accuracy and see how the model fits, learns, and fine-tunes hyperparameters.
At this stage, the model only considers the validation data, but it has not been used to learn the model's weights and biases. The test set accounts for 10% to 15% of the entire dataset. This is used for the final evaluation, as a controlled set, without bias.
The second stage of the testing process is tuning hyperparameters. During this phase, developers can control the behavior of the training algorithm and adjust parameters based on the results of the first phase. In the context of artificial intelligence and deep learning, possible hyperparameters might include learning rate, convolution kernel width, number of hidden units, regularization techniques, etc.
Finally, performing batch normalization involves two techniques: normalization and standardization so that the data is transformed by the same scale during training preparation. Once an AI model is sufficiently trained, fine-tuned, and standardized, its performance should be measured through confusion matrix, AUC ROC, F1 score, and other precision/accuracy metrics. Going through this rigorous process is critical to understanding how effectively and accurately your algorithms perform.
4. Security testing
Testing the security of your AI applications requires a combination of traditional security testing methods and considerations specific to AI systems. Consider the following points to start with:
- Identify security goals and risks: Identify security goals and potential risks associated with AI applications. Consider aspects such as data privacy, model integrity, adversarial attacks, and robustness to input changes. This step will help shape your testing strategy.
- Data Security: Evaluate data security for training, validation, and inference. Assess data privacy, storage, processing practices and access controls. Ensure sensitive data is properly protected and complies with privacy regulations.
- System Architecture and Infrastructure: Analyze the architecture and infrastructure of artificial intelligence applications. Consider security aspects such as authentication, authorization, and encryption. Verify that security best practices have been followed in the design and implementation of the system.
- Input Validation and Sanitization: Be aware of input validation and sanitization mechanisms. Verify that the application handles input data correctly to prevent common vulnerabilities such as injection attacks or buffer overflows.
- Third-party components: Assess the security of any third-party libraries, frameworks, or components used in AI applications. Make sure they are up to date, have no known vulnerabilities, and are properly configured.
- Documentation and Reporting: Document your findings, recommendations, and test results. Create comprehensive security testing reports outlining identified vulnerabilities, risks, and mitigations.
5. Performance and scalability testing
To perform performance testing on AI applications, it is essential to have a comprehensive understanding of the application's architecture, components and data flow. Capacity testing, durability testing, and stress testing are the most important types of performance testing that must be performed on AI applications to evaluate their performance and scalability.
This can be achieved with different test data, including large and small test data sets, because a large amount of test data will occupy more computing resources. Additionally, parallel monitoring of hardware resources helps to set the correct configuration to support expected user requests for AI applications.
6. Chatbot Testing
As chatbots become more popular in AI applications, it is critical to ensure that the information these bots provide to users is accurate. If your business uses chatbot functionality, you must test both the functional and non-functional components of the chatbot.
- Domain Tested: Chatbots are designed to deal with a specific domain or topic. Domain testing involves thoroughly testing the chatbot in scenarios relevant to its assigned domain. This ensures that the chatbot understands and accurately responds to queries within its intended scope.
- Boundary testing: Boundary testing evaluates how the chatbot handles inappropriate or unexpected user input. This includes testing chatbot responses to invalid or nonsensical questions and identifying outcomes when chatbots encounter glitches or errors. Boundary testing helps uncover potential bugs and improves error handling and user experience.
- The Conversational Factor: Chatbots rely on the flow of conversations to provide meaningful and engaging interactions. Validating different dialogue flows is critical to assessing chatbot responses in various scenarios. This includes evaluating the chatbot's ability to understand user intent, handle multiple turns in a conversation, and provide relevant and coherent responses. Evaluating dialogue factors can help optimize a chatbot's dialogue skills and enhance user experience.
7. Robot testing
Robot testing involves simulating real-world scenarios and evaluating the behavior of systems or algorithms in those scenarios. Simulation-based behavioral testing includes algorithm debugging, object detection, response testing, and validation of defined goals.
To ensure thorough testing, you should use low-fidelity 2D simulations and high-fidelity 3D simulations. The former is used for module-level behavior testing, and the latter is used for system-level behavior testing. This allows you to examine different levels of complexity and accuracy in simulations. The process also tests hardware availability scenarios and hardware unavailability scenarios. These scenarios evaluate the behavior and performance of a system or algorithm under different hardware conditions, ensuring robustness and adaptability in different environments.
8. Prioritize testing
The testing of AI/ML applications is very different from traditional software testing, and there are some technical challenges. However, with the birth of more and more AI/ML applications, the testing methods and practices for AI/ML applications are also developing rapidly and gradually improving.
The above are some effective and reasonable testing methods and ideas for AI/ML applications so far. If your business uses or offers AI solutions, you must prioritize a comprehensive testing approach to ensure accuracy, safety and inclusion.